This course will cover description of the uincertainty of hidden variables (parameters and state of a dynamic system) using
the probability language and methods for their estimation. Based on bayesian prblem formulation principles of rational behsavour
under uncertainty will be analysed and used to develp algorithms for estimation of parameters of ARX models and Kalman filtering
including the extensions.

We will demonstrate numerically robust implementation of the algorithms applicable in real life problems for the areas of
industrial process control, robotics and avionics. We will extend the methods for linear gaussian systems to a more generic
problems using Monte Calro approach. The course will also cover multimodel approach and its use for the fault detection and
isolation and introduction to adaptive control.

Requirements:

Syllabus of lectures:

1.Problem formulation, estimation methods

2.Bayesian approach to uncertainty description

3.Dynamic system model, probabilistic state definition

4.Identification of ARX model parameters

5.Tracking of time varuing parameters, forgetting, role of prior informaiton.